scholarly journals The Capacity of the Road Network: Data Collection and Statistical Analysis of Traffic Characteristics

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1765 ◽  
Author(s):  
Vladimir Shepelev ◽  
Sergei Aliukov ◽  
Kseniya Nikolskaya ◽  
Salavat Shabiev
Keyword(s):  
Data Collection ◽  
Road Traffic ◽  
Negative Consequences ◽  
The Road ◽  
Traffic Characteristics ◽  
Traffic Capacity ◽  
Traffic Data Collection ◽  
Fuzzy Logic Method ◽  
Additional Fuel Consumption ◽  
Additional Fuel

The possibilities of collecting the necessary information using multi-touch cameras and ways to improve road traffic data collection are considered. An increase in the number of vehicles leads to traffic jams, which in turn leads to an increase in travel time, additional fuel consumption and other negative consequences. To solve this problem, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The technology considered in the article allows taking into account pedestrians crossing the intersection. The purpose of this article is to determine the most important traffic characteristics that affect the traffic capacity of the intersection, in other words, the actual number of passing cars. Throughput is taken as a dependent variable. Based on the results of the regression analysis, a model was developed to predict the intersection throughput taking into account the most important traffic characteristics. Besides, this model is based on the fuzzy logic method and using the Fuzzy TECH 5.81d Professional Edition computer program.

scholarly journals The Use of Multi-Sensor Video Surveillance System to Assess the Capacity of the Road Network

2020 ◽  
Vol 21 (1) ◽  
pp. 15-31 ◽  
Author(s):  
Vladimir Shepelev ◽  
Sergei Aliukov ◽  
Kseniya Nikolskaya ◽  
Arkaprava Das ◽  
Ivan Slobodin
Keyword(s):  
Video Surveillance ◽  
Traffic Congestion ◽  
Surveillance Systems ◽  
Negative Consequences ◽  
Factors Affecting ◽  
Video Cameras ◽  
The Road ◽  
Scaling Methods ◽  
Additional Fuel Consumption ◽  
Additional Fuel

AbstractCurrently, in many cities around the world there is a significant increase in the number of vehicles, which leads to an aggravation of problems and contradictions in the road and transport system. This is especially true of traffic congestion, since the presence of the congestion leads to a number of negative consequences: an increase in travel time, additional fuel consumption and vehicle wear, stress and irritation of drivers and passengers, environmental poisoning and others. To solve the problem of congestion, it is necessary to have a reliable system for collecting information about the situation on the roads and a well-developed method for analyzing the collected information. The paper discusses the possibilities of collecting the required information using multi-touch video cameras and ways to improve them. A distinctive feature of this study is the registration of pedestrians crossing the road at the intersection. The aim of the work is to develop methods for collecting information using road sensor video surveillance systems in a traffic congestion and data processing using statistical methods such as: multiple regression analysis, cluster analysis, multidimensional scaling methods and others. The tasks were set: 1) to identify the most significant factors affecting the intensity of movement of vehicles at intersections in a congestion; 2) divide congestion into clusters with the identification of their characteristics; 3) to give a visual representation of multidimensional statistical information obtained with the help of multi-touch road video cameras.

scholarly journals Extracting Road Traffic Volume in the City before and during covid-19 through Video Remote Sensing

2021 ◽  
Vol 13 (12) ◽  
pp. 2329
Author(s):  
Elżbieta Macioszek ◽  
Agata Kurek
Keyword(s):  
Travel Behavior ◽  
Traffic Management ◽  
Road Traffic ◽  
Traffic Volume ◽  
The Road ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
On The Road ◽  
The Impact ◽  
The City

Continuous, automatic measurements of road traffic volume allow the obtaining of information on daily, weekly or seasonal fluctuations in road traffic volume. They are the basis for calculating the annual average daily traffic volume, obtaining information about the relevant traffic volume, or calculating indicators for converting traffic volume from short-term measurements to average daily traffic volume. The covid-19 pandemic has contributed to extensive social and economic anomalies worldwide. In addition to the health consequences, the impact on travel behavior on the transport network was also sudden, extensive, and unpredictable. Changes in the transport behavior resulted in different values of traffic volume on the road and street network than before. The article presents road traffic volume analysis in the city before and during the restrictions related to covid-19. Selected traffic characteristics were compared for 2019 and 2020. This analysis made it possible to characterize the daily, weekly and annual variability of traffic volume in 2019 and 2020. Moreover, the article attempts to estimate daily traffic patterns at particular stages of the pandemic. These types of patterns were also constructed for the weeks in 2019 corresponding to these stages of the pandemic. Daily traffic volume distributions in 2020 were compared with the corresponding ones in 2019. The obtained results may be useful in terms of planning operational and strategic activities in the field of traffic management in the city and management in subsequent stages of a pandemic or subsequent pandemics.

City Occupancy and Road Capacity Model

2014 ◽  
Vol 1044-1045 ◽  
pp. 1538-1540 ◽  
Author(s):  
Yi Lin Fan ◽  
Bei Chen Cheng ◽  
Li Li Cao ◽  
Kun Cheng Huang
Keyword(s):  
Cross Section ◽  
Road Traffic ◽  
The Road ◽  
Road Capacity ◽  
Traffic Capacity ◽  
Capacity Model ◽  
The Cross ◽  
Definition Of ◽  
The Impact ◽  
Accident Case

In this paper, we study the impact of city driveway occupancy on the urban road traffic capacity. Through the analysis of the scene of an accident case, we gained the vehicle statistics within a cross section in unit time and then did calculation and simulation. The work is concluded as a reasonable model of vehicle queue relationship evaluation.First, on the base of the definition of traffic capacity, statistics of the cars in the cross section before the accident, during the accident and after the accident have been respectively acquired in unit time (1 min). Then we employ Matlab to do the interpolation fitting, drawing the change of actual capacity of the road from the beginning time of an accident to the time when traffic resumes. The work comes to a conclusion that the change of the capacity at the cross section has a pattern of periodic fluctuation.

scholarly journals MTCS: A Method of Analyzing the Traffic Capacity of Urban Bus Lane Based on Vissim Simulation

2021 ◽  
Author(s):  
Hongtao Yuan ◽  
Huizhen Zhang ◽  
Minglei Liu ◽  
Cheng Wang ◽  
Yubiao Pan ◽  
...  
Keyword(s):  
Simulation Model ◽  
Traffic Flow ◽  
Public Transport ◽  
Empirical Formula ◽  
Road Traffic ◽  
The Road ◽  
Traffic Capacity ◽  
Urban Public Transport ◽  
Bus Lane ◽  
Urban Bus

Abstract As an effective method of improving the attractiveness of urban public transport and alleviating urban traffic congestion, bus lanes play an important role in the urban public transport system. The research on the capacity of bus lanes is conducive to improve the operation efficiency of urban bus roads and improve the service level of urban public transport. To obtain the maximum capacity of the bus lane, on one hand, the empirical formula can be used for theoretical calculation, and on the other hand, the simulation model can be established for analysis and verification. Based on the idea of simulation, a method using Vissim is proposed, called MTCS (Minimum Traffic Capacity Substitution Method). The method divides the bus lane into different sections by intersections and stops, establishes simulation model of the bus lane to calculate the traffic capacity of each section such as vehicle speed and flow and select the minimum traffic capacity of the sections as the traffic capacity of the bus lane, which is verified by using the road saturation. The simulation process uses the actual travel speed and traffic flow of the bus lane as evaluation indicators, with the aim of maximizing the road traffic flow while the actual speed of vehicles on the road is close to the desired speed, thus achieving the desired road traffic state. To verify and improve the effectiveness of the method, its analysis results are compared with the empirical formula, and various methods of enhancing traffic capacity are quantitatively simulated. The parameters of the simulation model are set by the actual bus lane example, and the experimental results show that by the methods of modifying the stop-station mode and the signal-lamp cycle, 10% and 14% improvements can be achieved, respectively. This has a good reference value for the construction of bus lanes and the adjustment of road facilities.

scholarly journals Overview of emerging road traffic data collection methods

2021 ◽  
Author(s):  
Sandra Mihalinac ◽  
Maja Ahac ◽  
Saša Ahac ◽  
Miroslav Šimun
Keyword(s):  
Data Collection ◽  
Traffic Flow ◽  
Road Network ◽  
Road Traffic ◽  
Traffic Volume ◽  
Transport Systems ◽  
Traffic Data ◽  
Data Collection Methods ◽  
Traffic Data Collection ◽  
Collection Methods

It is a well-known fact that the data on road traffic flow characteristics is essential for sustainable road network management. First road traffic volume counts date back to the 1950s when short-term periodic road traffic counts were carried out in cities worldwide. Manual traffic counting is one of the oldest and most technologically simple methods to obtain data on road traffic volume and its composition. Today, because of the ever-growing road transport demand, it has become clear that the development of Intelligent Transport Systems (ITS) is vital to increase safety and tackle increasing emission and congestion problems. The introduction of ITS highly depends on the quality and quantity of traffic data. Under the growing requirement of long-term traffic flow information, various traffic data collection methods have evolved. They allow systematic recording of the traffic flow volume and composition but also vehicle speed, total gross weight, number of axles, axle load and travel destination. This data, which is collected continuously over longer periods, enables a detailed analysis of traffic flows, and represents the basis for decision making in planning, designing, construction and maintenance of road infrastructure. This paper gives an overview of traditional and emerging traffic data collection methods - both fixed and mobile - and the analysis of the current road traffic data collection methods used on the Croatian road network, in terms of their potential and limitations.

Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection

2020 ◽  
Vol 47 (8) ◽  
pp. 982-997
Author(s):  
Mohamed H. Zaki ◽  
Tarek Sayed ◽  
Moataz Billeh
Keyword(s):  
Data Collection ◽  
Video Analysis ◽  
Three Dimensional ◽  
Ground Truth ◽  
Traffic Data ◽  
Ground Truth Data ◽  
The Road ◽  
Traffic Data Collection ◽  
Bounding Boxes ◽  
Automated Video Analysis

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements.

Traffic Data Collection and Visualization Tool for Knowledge Discovery Using Google Maps

2022 ◽  
Vol 10 (1) ◽  
pp. 1-12
Author(s):  
Iftekhar Hossain ◽  
Naushin Nower
Keyword(s):  
Data Collection ◽  
Traffic Modeling ◽  
Traffic Data ◽  
Google Maps ◽  
Traffic Jam ◽  
The Road ◽  
Traffic Forecasting ◽  
Traffic Data Collection ◽  
Data Collection And Analysis ◽  
Flow Intensity

Traffic jam is increasingly aggravating in almost every urban area. Traffic forecast, traffic modeling, visualization can help to provide appropriate route and time for traveling and thus provides a significant impact on traffic jam reduction. For traffic forecasting, modeling and visualization, city-wide traffic data collection and analysis are needed, which is still challenging in many aspects. This paper aims to develop a tool for acquiring and processing traffic data from Google Maps that can be used for forecasting, modeling, and visualization. Dhaka city is used as a case study since there is no infrastructure available for traffic data collection. The traffic flow intensity of the road is analyzed to determine the congestion of the road. The flow intensity is used for traffic modeling, visualization, traffic prediction and many more.

Effects of Lanes Occupied on Urban Road Traffic Capacity

2014 ◽  
Vol 926-930 ◽  
pp. 3798-3801
Author(s):  
Zhi Wei Yang
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Flow Speed ◽  
Urban Traffic ◽  
Correction Coefficient ◽  
Dynamic Changes ◽  
Traffic Jam ◽  
The Road ◽  
Discharge Capacitance ◽  
Traffic Capacity

The article is research on the influence of urban lane occupied for the road traffic capacity. Under the condition that the density of urban traffic flow is big, and it‘s successional, we consider the quantity of vehicle is continuous. Through analyzing the dynamic changes of the road traffic capacity and its influencing factors after accidents, we can get reasonable suggestions of reducing the length of traffic jam. First we establish a flow-speed-density model to describe the dynamic changes of the road traffic capacity. Then we can compare the traffic flow to the electric current according to its continuity. So the upstream traffic flow and the traffic capacity of the accident cross section are equal to the charging current and the discharging current. And the vehicle queue is translated to the voltage of the charge-discharge capacitance. We can get the length of the vehicle queue by the formula of the capacitance voltage approximately. Finally the correction coefficient is introduced. In conclusion, the road traffic capacity is depended on the distance from the upstream intersection and the lane that the accident happened on and so on. Meanwhile, if we don’t solve the accident timely, the length will rise sharply. It will cause serious traffic jam. So we suggest relevant departments timely deal with the accident, evacuate the traffic, and prompt drivers to change lanes in advance.

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